SELECT TCID, START_TIME, RESULT,
cast(START_TIME as date) as m_date,
max(cast(START_TIME as time)) as max_time
FROM jenkins_result.JENKINS_RESULT
WHERE TCID = 'A330506'
GROUP BY TCID, m_date;
This is my data:
ID TCID START_DATE RESULT
1545240 A435727 2020-11-08 03:11:43 PASS
1545334 A435727 2020-11-08 03:19:53 PASS
1547439 A435727 2020-11-09 03:11:52 PASS
1547621 A435727 2020-11-09 03:20:05 PASS
1548388 A435727 2020-11-09 07:51:29 PASS
1558801 A435727 2020-11-12 00:11:10 PASS
1561899 A435727 2020-11-12 08:48:59 PASS
I want to get result of each TCID follow date like this
ID TCID START_DATE RESULT
1545334 A435727 2020-11-08 03:19:53 PASS
1548388 A435727 2020-11-09 07:51:29 PASS
1561899 A435727 2020-11-12 08:48:59 PASS
But the result current like that:
1545240 A435727 2020-11-08 03:11:43 PASS 2020-11-08 03:19:53
1547439 A435727 2020-11-09 03:11:52 PASS 2020-11-09 07:51:29
1558801 A435727 2020-11-12 00:11:10 PASS 2020-11-12 08:48:59
def connect_cli_server(self):
connect_success = 0
if self.ssh_client is None:
self.ssh_client = paramiko.SSHClient()
self.ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy)
for cnt in range(self.retry_cnt):
try:
self.ssh_client.connect(self.ip, 22, self.id, self.pw, timeout=self.time_out,
banner_timeout=self.banner)
connect_success = 1
break
except:
if cnt < 10:
time.sleep(random.uniform(0.1, 0.3))
if 10 <= cnt < 20:
time.sleep(random.uniform(0.1, 1))
else:
time.sleep(random.uniform(0.5, 1.5))
continue
if not connect_success:
try:
self.connect_cli_server_thru_remote_server()
except Exception as error:
print(error)
return False
return True
def send_command(self, ssh_client, command):
chan = ssh_client.get_transport().open_session()
chan.get_pty()
fileobject = chan.makefile()
chan.exec_command(command)
byteoutput = fileobject.read()
convetedstring = byteoutput.decode("UTF-8")
return convetedstring
I created some sample input by sql command
create table tbl_mock (
id int,
tc int,
startdate datetime,
result varchar(20)
);
insert into tbl_mock(id, tc, startdate, result) values (1, 1, '2020/11/12 09:00:00', 'pass');
insert into tbl_mock(id, tc, startdate, result) values (2, 1, '2020/11/12 10:00:00', 'fail');
insert into tbl_mock(id, tc, startdate, result) values (3, 1, '2020/11/12 11:00:00', 'pass');
insert into tbl_mock(id, tc, startdate, result) values (4, 1, '2020/11/13 09:00:00', 'pass');
insert into tbl_mock(id, tc, startdate, result) values (5, 1, '2020/11/13 10:00:00', 'fail');
insert into tbl_mock(id, tc, startdate, result) values (6, 1, '2020/11/13 11:00:00', 'fail');
You can try the below sql command to get your result
select tbl_a.*
from tbl_mock as tbl_a,
(select tc,
cast(startdate as date) as m_date,
max(cast(startdate as time)) as m_time
from tbl_mock
group by tc, m_date) as tbl_b
where tbl_a.tc = tbl_b.tc
and timestamp(tbl_b.m_date, tbl_b.m_time) = tbl_a.startdate
You can try this:
SELECT TCID
,START_TIME
,RESULT
,cast(START_TIME as date) as m_date
,max(cast(START_TIME as time)) as max_time
FROM jenkins_result.JENKINS_RESULT
WHERE TCID='A330506'
GROUP BY TCID
,START_TIME
,RESULT
,cast(START_TIME as date)
ORDER BY TCID
,m_date;
which should be the same as this:
SELECT DISTINCT TCID
,START_TIME
,RESULT
,cast(START_TIME as date) as m_date
,max(cast(START_TIME as time)) OVER() as max_time
FROM jenkins_result.JENKINS_RESULT
WHERE TCID='A330506'
ORDER BY TCID
,m_date;
or if you need to get the MAX value per TCID:
SELECT DISTINCT TCID
,START_TIME
,RESULT
,cast(START_TIME as date) as m_date
,max(cast(START_TIME as time)) OVER(PARTITION BY TCID) as max_time
FROM jenkins_result.JENKINS_RESULT
WHERE TCID='A330506'
ORDER BY TCID
,m_date;
import pygame
import os
import numpy as np
import queue
pygame.init()
q = 8
w = 70
def normalize_image(img):
return pygame.transform.scale(img, (w - 10, w - 10))
agent = pygame.image.load(os.path.join('EmptyAgent.png'))
wumpus = pygame.image.load(os.path.join('Wumpus.png'))
gold = pygame.image.load(os.path.join('Gold.png'))
pit = pygame.image.load(os.path.join('Pit.png'))
agent = normalize_image(agent)
wumpus = normalize_image(wumpus)
gold = normalize_image(gold)
pit = normalize_image(pit)
gr_empty = 0
gr_agent = 4
gr_wumpus = 1
gr_gold = 3
gr_pit = 2
clock = pygame.time.Clock()
screen = pygame.display.set_mode((q * w, q * w))
xh, yh = q - 1, 0
grid = [[gr_empty] * q for _ in range(q)]
grid[xh][yh] = gr_agent
bg_color = (255, 255, 255)
border_color = (0, 0, 0)
dx = [0,1,0,-1]
dy = [1,0,-1,0]
def bfs():
vs = np.zeros((q,q,8))
tracex = np.zeros((q,q,8))
tracey = np.zeros((q,q,8))
traced = np.zeros((q,q,8))
tracex[0][0][0] = 2
que = queue.Queue()
que.put((q-1,0,0,0))
vs[q-1][0][0]=1
gx, gy, gd, gt = 0, 0, 0, 1000
while not que.empty():
(x,y,dir,t) = que.get()
d=0
if grid[x][y]==3 and dir<4:
if gt>t:
gx, gy, gd, gt = x,y,dir,t
if vs[x][y][dir+4]==0:
que.put((x,y,dir+4,t+1))
vs[x][y][dir+4]=1
tracex[x][y][dir+4],tracey[x][y][dir+4],traced[x][y][dir+4] = x,y,dir
continue
if x==q-1 and y==0 and dir>3:
return 1000-(t+1)*10, gx, gy, gd, tracex, tracey, traced
if x<0 or y<0 or x>=q or y>=q:
continue
if grid[x][y]==gr_wumpus or grid[x][y]==gr_pit:
continue
if dir>3:
d+=4
dir-=4
if vs[x][y][(dir + 1 + 4) % 4 + d] == 0:
que.put((x, y, (dir + 1 + 4) % 4 + d, t + 1))
vs[x][y][(dir + 1 + 4) % 4 + d] = 1
tracex[x][y][(dir + 1 + 4) % 4 + d], tracey[x][y][(dir + 1 + 4) % 4 + d], traced[x][y][(dir + 1 + 4) % 4 + d] = x, y, dir
if vs[x][y][(dir - 1 + 4) % 4 + d] == 0:
que.put((x, y, (dir - 1 + 4) % 4 + d, t + 1))
vs[x][y][(dir - 1 + 4) % 4 + d] = 1
tracex[x][y][(dir - 1 + 4) % 4 + d], tracey[x][y][(dir - 1 + 4) % 4 + d], traced[x][y][(dir - 1 + 4) % 4 + d] = x, y, dir
if x+dx[dir]<0 or x+dx[dir]>=q or y+dy[dir]<0 or y+dy[dir]>=q:
continue
if vs[x + dx[dir]][y + dy[dir]][dir + d] == 0:
que.put((x + dx[dir], y + dy[dir], dir + d, t + 1))
vs[x + dx[dir]][y + dy[dir]][dir + d] = 1
tracex[x + dx[dir]][y + dy[dir]][dir + d], tracey[x + dx[dir]][y + dy[dir]][dir + d], traced[x + dx[dir]][y + dy[dir]][dir + d] = x, y, dir
return -1, gx, gy, gd, tracex, tracey, traced
def test():
grid[q-2][1] = gr_wumpus
grid[q-3][2] = gr_gold
grid[q-3][0] = gr_pit
ans, gx, gy, gd, tracex, tracey, traced = bfs()
if ans<0:
ans=-1
print(ans)
tx, ty, td = gx, gy, gd
if ans>=0:
while 1:
print(tx, ty, td)
if tx==q-1 and ty==0:
break
xn, yn, dn = int(tracex[tx][ty][td]), int(tracey[tx][ty][td]), int(traced[tx][ty][td])
tx, ty, td = xn,yn,dn
# print(tracex[0][0][0])
def draw_img(img, x, y):
screen.blit(img, (x + 5, y + 5))
def draw():
x, y = 0, 0
screen.fill(bg_color)
for i in range(q):
for j in range(q):
if grid[i][j] == gr_agent:
draw_img(agent, x, y)
elif grid[i][j] == gr_wumpus:
draw_img(wumpus, x, y)
elif grid[i][j] == gr_gold:
draw_img(gold, x, y)
elif grid[i][j] == gr_pit:
draw_img(pit, x, y)
for k in range(4):
pygame.draw.rect(screen, border_color, (x - k, y - k, w, w), 1)
x = x + w
x = 0
y = y + w
def main():
test()
while True:
events = pygame.event.get()
keys = pygame.key.get_pressed()
for event in events:
if event.type == pygame.QUIT:
return
elif event.type == pygame.MOUSEBUTTONDOWN:
mx, my = event.pos
gy = mx // w
gx = my // w
if not grid[gx][gy] == gr_empty:
continue
elif event.button == 1:
# Left click
grid[gx][gy] = gr_wumpus
elif event.button == 3:
# Scroll slick
grid[gx][gy] = gr_gold
elif event.button == 2:
# Middle click
grid[gx][gy] = gr_pit
draw()
pygame.display.flip()
clock.tick(600)
main()
import pygame
import os
import numpy as np
import queue
pygame.init()
q = 3
w = 70
def normalize_image(img):
return pygame.transform.scale(img, (w - 10, w - 10))
agent = pygame.image.load(os.path.join('EmptyAgent.png'))
wumpus = pygame.image.load(os.path.join('Wumpus.png'))
gold = pygame.image.load(os.path.join('Gold.png'))
pit = pygame.image.load(os.path.join('Pit.png'))
agent = normalize_image(agent)
wumpus = normalize_image(wumpus)
gold = normalize_image(gold)
pit = normalize_image(pit)
gr_empty = 0
gr_agent = 4
gr_wumpus = 1
gr_gold = 3
gr_pit = 2
clock = pygame.time.Clock()
screen = pygame.display.set_mode((q * w, q * w))
xh, yh = q - 1, 0
grid = [[gr_empty] * q for _ in range(q)]
grid[xh][yh] = gr_agent
bg_color = (255, 255, 255)
border_color = (0, 0, 0)
dx = [0,1,0,-1]
dy = [1,0,-1,0]
def bfs():
vs = np.zeros((q,q,8))
tracex = np.zeros((q,q,8))
tracey = np.zeros((q,q,8))
traced = np.zeros((q,q,8))
tracex[0][0][0] = 2
que = queue.Queue()
que.put((q-1,0,0,0))
vs[q-1][0][0]=1
gx, gy, gd, gt = 0, 0, 0, 1000
while not que.empty():
(x,y,dir,t) = que.get()
d=0
if grid[x][y]==3 and dir<4:
if gt>t:
gx, gy, gd, gt = x,y,dir,t
if vs[x][y][dir+4]==0:
que.put((x,y,dir+4,t+1))
vs[x][y][dir+4]=1
tracex[x][y][dir+4],tracey[x][y][dir+4],traced[x][y][dir+4] = x,y,dir
continue
if x==q-1 and y==0 and dir>3:
gx, gy, gd, gt = x, y, dir, t
return 1000-(t+1)*10, gx, gy, gd, tracex, tracey, traced
if x<0 or y<0 or x>=q or y>=q:
continue
if grid[x][y]==gr_wumpus or grid[x][y]==gr_pit:
continue
if dir>3:
d+=4
dir-=4
if vs[x][y][(dir + 1 + 4) % 4 + d] == 0:
que.put((x, y, (dir + 1 + 4) % 4 + d, t + 1))
vs[x][y][(dir + 1 + 4) % 4 + d] = 1
tracex[x][y][(dir + 1 + 4) % 4 + d], tracey[x][y][(dir + 1 + 4) % 4 + d], traced[x][y][(dir + 1 + 4) % 4 + d] = x, y, dir + d
if vs[x][y][(dir - 1 + 4) % 4 + d] == 0:
que.put((x, y, (dir - 1 + 4) % 4 + d, t + 1))
vs[x][y][(dir - 1 + 4) % 4 + d] = 1
tracex[x][y][(dir - 1 + 4) % 4 + d], tracey[x][y][(dir - 1 + 4) % 4 + d], traced[x][y][(dir - 1 + 4) % 4 + d] = x, y, dir + d
if x+dx[dir]<0 or x+dx[dir]>=q or y+dy[dir]<0 or y+dy[dir]>=q:
continue
if vs[x + dx[dir]][y + dy[dir]][dir + d] == 0:
que.put((x + dx[dir], y + dy[dir], dir + d, t + 1))
vs[x + dx[dir]][y + dy[dir]][dir + d] = 1
tracex[x + dx[dir]][y + dy[dir]][dir + d], tracey[x + dx[dir]][y + dy[dir]][dir + d], traced[x + dx[dir]][y + dy[dir]][dir + d] = x, y, dir + d
return -1, gx, gy, gd, tracex, tracey, traced
def test():
grid[q-2][1] = gr_wumpus
grid[q-3][2] = gr_gold
grid[q-3][0] = gr_pit
ans, gx, gy, gd, tracex, tracey, traced = bfs()
if ans<0:
ans=-1
print(ans)
tx, ty, td = gx, gy, gd
if ans>=0:
while 1:
print(tx, ty, td)
if tx==q-1 and ty==0 and td==0:
break
xn, yn, dn = int(tracex[tx][ty][td]), int(tracey[tx][ty][td]), int(traced[tx][ty][td])
tx, ty, td = xn,yn,dn
# print(tracex[0][0][0])
def draw_img(img, x, y):
screen.blit(img, (x + 5, y + 5))
def draw():
x, y = 0, 0
screen.fill(bg_color)
for i in range(q):
for j in range(q):
if grid[i][j] == gr_agent:
draw_img(agent, x, y)
elif grid[i][j] == gr_wumpus:
draw_img(wumpus, x, y)
elif grid[i][j] == gr_gold:
draw_img(gold, x, y)
elif grid[i][j] == gr_pit:
draw_img(pit, x, y)
for k in range(4):
pygame.draw.rect(screen, border_color, (x - k, y - k, w, w), 1)
x = x + w
x = 0
y = y + w
def main():
test()
while True:
events = pygame.event.get()
keys = pygame.key.get_pressed()
for event in events:
if event.type == pygame.QUIT:
return
elif event.type == pygame.MOUSEBUTTONDOWN:
mx, my = event.pos
gy = mx // w
gx = my // w
if not grid[gx][gy] == gr_empty:
continue
elif event.button == 1:
# Left click
grid[gx][gy] = gr_wumpus
elif event.button == 3:
# Scroll slick
grid[gx][gy] = gr_gold
elif event.button == 2:
# Middle click
grid[gx][gy] = gr_pit
draw()
pygame.display.flip()
clock.tick(600)
main()
Related
these is my movie ticketing Code
x = 10
Booked_seat = 0
prize_of_ticket = 0
Total_Income = 0
Row = int(input('Enter number of Row - \n'))
Seats = int(input('Enter number of seats in a Row - \n'))
Total_seat = Row*Seats
Booked_ticket_Person = [[None for j in range(Seats)] for i in range(Row)]
class chart:
#staticmethod
def chart_maker():
seats_chart = {}
for i in range(Row):
seats_in_row = {}
for j in range(Seats):
seats_in_row[str(j+1)] = 'S'
seats_chart[str(i)] = seats_in_row
return seats_chart
#staticmethod
def find_percentage():
percentage = (Booked_seat/Total_seat)*100
return percentage
class_call = chart
table_of_chart = class_call.chart_maker()
while x != 0:
print('1 for Show the seats \n2 for Buy a Ticket \n3 for Statistics ',
'\n4 for Show booked Tickets User Info \n0 for Exit')
x = int(input('Select Option - '))
if x == 1:
if Seats < 10:
for seat in range(Seats):
print(seat, end=' ')
print(Seats)
else:
for seat in range(10):
print(seat, end=' ')
for seat in range(10, Seats):
print(seat, end=' ')
print(Seats)
if Seats < 10:
for num in table_of_chart.keys():
print(int(num)+1, end=' ')
for no in table_of_chart[num].values():
print(no, end=' ')
print()
else:
count_num = 0
for num in table_of_chart.keys():
if int(list(table_of_chart.keys())[count_num]) < 9:
print(int(num)+1, end=' ')
else:
print(int(num)+1, end=' ')
count_key = 0
for no in table_of_chart[num].values():
if int(list(table_of_chart[num].keys())[count_key]) <= 10:
print(no, end=' ')
else:
print(no, end=' ')
count_key += 1
count_num += 1
print()
print('Vacant Seats = ', Total_seat - Booked_seat)
print()
elif x == 2:
Row_number = int(input('Enter Row Number - \n'))
Column_number = int(input('Enter Column Number - \n'))
if Row_number in range(1, Row+1) and Column_number in range(1, Seats+1):
if table_of_chart[str(Row_number-1)][str(Column_number)] == 'S':
if Row*Seats <= 60:
prize_of_ticket = 10
elif Row_number <= int(Row/2):
prize_of_ticket = 10
else:
prize_of_ticket = 8
print('prize_of_ticket - ', '$', prize_of_ticket)
conform = input('yes for booking and no for Stop booking - ')
person_detail = {}
if conform == 'yes':
person_detail['Name'] = input('Enter Name - ')
person_detail['Gender'] = input('Enter Gender - ')
person_detail['Age'] = input('Enter Age - ')
person_detail['Phone_No'] = input('Enter Phone number - ')
person_detail['Ticket_prize'] = prize_of_ticket
table_of_chart[str(Row_number-1)][str(Column_number)] = 'B'
Booked_seat += 1
Total_Income += prize_of_ticket
else:
continue
Booked_ticket_Person[Row_number-1][Column_number-1] = person_detail
print('Booked Successfully')
else:
print('This seat already booked by some one')
else:
print()
print('*** Invalid Input ***')
print()
elif x == 3:
print('Number of purchased Ticket - ', Booked_seat)
print('Percentage - ', class_call.find_percentage())
print('Current Income - ', '$', prize_of_ticket)
print('Total Income - ', '$', Total_Income)
print()
elif x == 4:
Enter_row = int(input('Enter Row number - \n'))
Enter_column = int(input('Enter Column number - \n'))
if Enter_row in range(1, Row+1) and Enter_column in range(1, Seats+1):
if table_of_chart[str(Enter_row-1)][str(Enter_column)] == 'B':
person = Booked_ticket_Person[Enter_row - 1][Enter_column - 1]
print('Name - ', person['Name'])
print('Gender - ', person['Gender'])
print('Age - ', person['Age'])
print('Phone number - ', person['Phone_No'])
print('Ticket Prize - ', '$', person['Ticket_prize'])
else:
print()
print('---**--- Vacant seat ---**---')
else:
print()
print('*** Invalid Input ***')
print()
else:
print()
print('*** Invalid Input ***')
print()
(i want To add Admin login panal and Customer login panel. How to do it??. also i want seperate things in Admin panal and customer panal, to Add first the 4 options into admin panal and Add First 2 options in Customer panal .. Thanks little help would be Very Helpfull <3
I have a function T which I'm wanting to maximise:
T = (B((A + 1.646) + 0.583 w) + 311)((1 + ((R + 0.553) + 0.0389 x)((D) + 0.0389 y)
with the constraints
w + x + y + z = 25
w >= 0
x >= 0
y >= 0
z >= 0
B, A, R, D are all constants.
How would I go about doing this?
I have the following vectors
shape u_w: (50,)
shape Vt: (6, 50)
shape v: (50,)
and with them I perform the following calculations
w = np.tanh(u_w + Vt[0])
w_squared = w ** 2
z = np.dot(v, w)
s = sigmoid(np.dot(v, w))
J = -np.log(sigmoid(z))
dv = np.dot(sigmoid(z) - 1, w)
du_w = np.dot(s - 1, v, (1 - w_squared))
dVt = np.dot(s - 1, v, (1 - w_squared))
for vt in Vt[1:]:
t = np.tanh(u_w + vt)
svt = sigmoid(np.dot(-v, t))
J -= np.log(svt)
dv -= np.dot((svt - 1), t)
du_w -= np.dot((svt - 1), v, (1 - t**2))
dVt = np.vstack((dVt, -np.dot(svt - 1, v, (1 - t**2))))
How do I vectorize the calculations for J, dv, du_w and dVt, so that they work for a batch of S items with the following shapes?
shape(u_w) => (512, 50)
shape(Vt) => (512, 6, 50)
shape(v) => (50,)
From deeplearning course on Coursera I've implemented logistic regression :
import numpy as np
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
def sigmoid(z):
s = 1 / (1 + np.exp(-z))
return s
def initialize_with_zeros(dim):
w = np.zeros(shape=(dim, 1))
b = 0
return w, b
def propagate(w, b, X, Y):
m = X.shape[1]
A = sigmoid(np.dot(w.T, X) + b) # compute activation
cost = (- 1 / m) * np.sum(Y * np.log(A) + (1 - Y) * (np.log(1 - A))) # compute cost
dw = (1 / m) * np.dot(X, (A - Y).T)
db = (1 / m) * np.sum(A - Y)
cost = np.squeeze(cost)
grads = {"dw": dw,
"db": db}
return grads, cost
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):
costs = []
for i in range(num_iterations):
grads, cost = propagate(w, b, X, Y)
dw = grads["dw"]
db = grads["db"]
w = w - learning_rate * dw # need to broadcast
b = b - learning_rate * db
if i % 100 == 0:
costs.append(cost)
# Print the cost every 100 training examples
if print_cost and i % 100 == 0:
print ("Cost after iteration %i: %f" % (i, cost))
params = {"w": w,
"b": b}
grads = {"dw": dw,
"db": db}
return params, grads, costs
def predict(w, b, X):
m = X.shape[1]
Y_prediction = np.zeros((1, m))
w = w.reshape(X.shape[0], 1)
A = sigmoid(np.dot(w.T, X) + b)
for i in range(A.shape[1]):
# Convert probabilities a[0,i] to actual predictions p[0,i]
### START CODE HERE ### (≈ 4 lines of code)
print(A)
Y_prediction[0, i] = 1 if A[0, i] > 0.5 else 0
### END CODE HERE ###
assert(Y_prediction.shape == (1, m))
return Y_prediction
print ("sigmoid(0) = " + str(sigmoid(0)))
print ("sigmoid(9.2) = " + str(sigmoid(9.2)))
dim = 2
w, b = initialize_with_zeros(dim)
print ("w = " + str(w))
print ("b = " + str(b))
w, b, X, Y = np.array([[1], [2]]), 2, np.array([[-1,-2], [3,4]]), np.array([[1, 0]])
grads, cost = propagate(w, b, X, Y)
print ("dw = " + str(grads["dw"]))
print ("db = " + str(grads["db"]))
print ("cost = " + str(cost))
params, grads, costs = optimize(w, b, X, Y, num_iterations= 10000, learning_rate = 0.01, print_cost = False)
print ("w = " + str(params["w"]))
print ("b = " + str(params["b"]))
print ("dw = " + str(grads["dw"]))
print ("db = " + str(grads["db"]))
print("predictions = " + str(predict(w, b, X)))
def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
w, b = initialize_with_zeros(X_train.shape[0])
parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
w = parameters["w"]
b = parameters["b"]
Y_prediction_test = predict(w, b, X_test)
Y_prediction_train = predict(w, b, X_train)
print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))
d = {"costs": costs,
"Y_prediction_test": Y_prediction_test,
"Y_prediction_train" : Y_prediction_train,
"w" : w,
"b" : b,
"learning_rate" : learning_rate,
"num_iterations": num_iterations}
return d
I'm attempting to use a generic dataset which contains 5 samples where each sample contain 4 elements :
train_set_x = np.array([[1,2,3,4],[4,3,2,1],[1,2,3,4],[4,3,2,1],[1,2,3,4]])
train_set_y = np.array([1,0,1,0,1])
test_set_x = np.array([[1,2,3,4],[4,3,2,1],[1,2,3,4],[4,3,2,1],[1,2,3,4]])
test_set_y = np.array([1,0,1,0,1])
train_set_x , train_set_y , test_set_x , test_set_y
d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
But the following error is thrown :
<ipython-input-409-bd4e233a8f4e> in propagate(w, b, X, Y)
18
19 A = sigmoid(np.dot(w.T, X) + b) # compute activation
---> 20 cost = (- 1 / m) * np.sum(Y * np.log(A) + (1 - Y) * (np.log(1 - A))) # compute cost
21
22 dw = (1 / m) * np.dot(X, (A - Y).T)
ValueError: operands could not be broadcast together with shapes (5,) (1,4)
Do I need to change the weight dimensions in order to compute the cost value ?
Update :
Using modification :
A = sigmoid(np.dot(X , w) + b) # compute activation
causes error :
<ipython-input-546-7a7980550834> in propagate(w, b, X, Y)
20 m = X.shape[1]
21
---> 22 A = sigmoid(np.dot(X , w) + b) # compute activation
23 print('w.T' , w.T , 'w' , w, 'X' , X , 'Y' , Y , 'A' , A)
24 cost = (- 1 / m) * np.sum(Y * np.log(A) + (1 - Y) * (np.log(1 - A))) # compute cost
ValueError: shapes (5,4) and (5,1) not aligned: 4 (dim 1) != 5 (dim 0)
I've discretised a diffusion equation with FEniCS as follows:
def DiscretiseEquation(h):
mesh = UnitSquareMesh(h, h)
V = FunctionSpace(mesh, 'Lagrange', 1)
def on_boundary(x, on_boundary):
return on_boundary
bc_value = Constant(0.0)
boundary_condition = DirichletBC(V, bc_value, on_boundary)
class RandomDiffusionField(Expression):
def __init__(self, m, n, element):
self._rand_field = np.exp(-np.random.randn(m, n))
self._m = m
self._n = n
self._ufl_element = element
def eval(self, value, x):
x_index = np.int(np.floor(self._m * x[0]))
y_index = np.int(np.floor(self._n * x[1]))
i = min(x_index, self._m - 1)
j = min(y_index, self._n - 1)
value[0] = self._rand_field[i, j]
def value_shape(self):
return(1, )
class RandomRhs(Expression):
def __init__(self, m, n, element):
self._rand_field = np.random.randn(m, n)
self._m = m
self._n = n
self._ufl_element = element
def eval(self, value, x):
x_index = np.int(np.floor(self._m * x[0]))
y_index = np.int(np.floor(self._n * x[1]))
i = min(x_index, self._m - 1)
j = min(y_index, self._n - 1)
value[0] = self._rand_field[i, j]
def value_shape(self):
return (1, )
u = TrialFunction(V)
v = TestFunction(V)
random_field = RandomDiffusionField(100, 100, element=V.ufl_element())
zero = Expression("0", element=V.ufl_element())
one = Expression("1", element=V.ufl_element())
diffusion = as_matrix(((random_field, zero), (zero, one)))
a = inner(diffusion * grad(u), grad(v)) * dx
L = RandomRhs(h, h, element=V.ufl_element()) * v * dx
A = assemble(a)
b = assemble(L)
boundary_condition.apply(A, b)
A = as_backend_type(A).mat()
(indptr, indices, data) = A.getValuesCSR()
mat = csr_matrix((data, indices, indptr), shape=A.size)
rhs = b.array()
#Solving
x = spsolve(mat, rhs)
#Conversion to a FEniCS function
u = Function(V)
u.vector()[:] = x
I am running the GMRES solver as normal. The callback argument is a separate iteration counter I've defined.
DiscretiseEquation(100)
A = mat
b = rhs
x, info = gmres(A, b, callback = IterCount())
The routine returns a NameError, stating that 'mat' is not defined:
NameError Traceback (most recent call last)
<ipython-input-18-e096b2eea097> in <module>()
1 DiscretiseEquation(200)
----> 2 A = mat
3 b = rhs
4 x_200, info_200 = gmres(A, b, callback = IterCount())
5 gmres_res = closure_variables["residuals"]
NameError: name 'mat' is not defined
As far as I'm aware, it should be defined when I call the DiscretiseEquation function?